On-Line Handwritten Chinese Character Recognition Based on Nested Segmentation of Radicals

This paper presents a radical-based on-line handwritten Chinese character recognition method, which integrates appearance-based radical recognition and geometric context into a principled framework using a character-radical dictionary to guide radical segmentation and recognition during path search. To solve the connection between radicals, we detect corner points to extract sub-strokes. Based on the hierarchical structure, the character pattern is over-segmented by three-layer nested pre-segmentation. For recognition, we use two dictionary representation schemes and accordingly different search algorithms. We have implemented the approach to Chinese characters of left-right and up-down structures. Experimental results on a sample set of 5,773 character classes consisting of 1,149 radicals demonstrate the effectiveness of our approach.